The next major shift in enterprise AI? Agentic AI — intelligent systems that don’t just process information but act on it. This evolution moves beyond automation, enabling AI agents to drive productivity, improve customer service, and reduce operational costs — all while working autonomously within enterprise-approved knowledge.
In today’s business landscape, CIOs, IT leaders, and digital executives face a dual challenge: balancing efficiency and compliance while accelerating innovation. Traditional automation helps, but it’s limited to predefined tasks.
Agentic AI systems go further. They anticipate needs, makes informed decisions, and continuously improve over time — without constant human intervention.
But this power comes with responsibility. Agentic AI functionality must be grounded in trusted enterprise knowledge, governed by security protocols, and aligned with business goals to deliver real impact.
In this post, we’ll break down:
- What agentic AI really is (and isn’t).
- How it differs from traditional and generative AI.
- The tangible benefits it brings to enterprises.
- Key considerations for adopting it effectively.
What Is Agentic AI?
Unlike traditional AI, which follows pre-set rules or generates static responses, agentic AI actively drives outcomes. It doesn’t just process information—it sets goals, makes decisions, and autonomously takes action to achieve business objectives.
The term “agentic” comes from “agency,” meaning the ability to act independently. Agentic AI isn’t just reacting — it’s proactively solving problems.
For enterprises, this means:
- Increased Efficiency: AI agents automate workflows, reducing manual effort and improving employee productivity.
- Smarter Decision-Making: AI continuously refines its strategies based on real-time data, ensuring better business outcomes.
- Higher Revenue, Lower Costs: AI-driven recommendations, dynamic decision-making, and self-improving systems help businesses scale efficiently.
Instead of simply responding to queries, agentic AI actively works toward defined goals — whether that’s resolving a customer issue before it escalates, optimizing an ecommerce journey in real time, or improving enterprise search by delivering hyper-relevant, context-aware results.
This marks a fundamental shift: AI that no longer just reacts to human input but anticipates needs and acts accordingly.
An early example of agentic AI is OpenAI’s recently launched “Operator”. Currently in research preview, Operator can browse the web, fill out forms, order groceries, and handle other repetitive online tasks. This marks one of OpenAI’s first steps toward AI that works independently, aiming to save users time and enhance digital interactions for businesses.
How Does Agentic AI Work?
AI isn’t just about answering questions — it can think, make decisions, and take action. Agentic AI follows a four-step process to solve problems and continuously improve over time:
- Perceive: AI agents gather real-time data from enterprise systems, customer interactions, and external sources. By extracting key signals and recognizing patterns, the AI ensures every decision is informed by relevant context.
- Reason: Powered by large language models (LLMs) and retrieval-augmented generation (RAG), agentic AI analyzes patterns, interprets intent, and ensures accuracy before taking action. This is critical for enterprises — LLMs alone can hallucinate, but retrieval techniques ensure AI decisions are based on trusted, up-to-date information.
- Act: AI agents integrate with enterprise applications to execute tasks, automate workflows, and optimize operations. Built-in governance and compliance controls ensure actions align with business policies.
- Learn: Every interaction fuels a continuous improvement loop. Over time, AI agents refine their decision-making, improving accuracy, efficiency, and adaptability — delivering more value with each interaction.
How Does Agentic AI Differ From Generative AI?
There’s a lot of AI out there, so it helps to be able to differentiate between different applications. While both agentic AI and Gen AI operate with large language models at their heart, there are a few differences in their AI capabilities.
Generative AI, as the name says, generates content in many forms. From ChatGPT’s Dall-E imagery to the wide variety of “answer” machines (from Anthropic’s Claude to Google’s Gemini to Coveo’s own enterprise-ready Relevance Generative Answering), GenAI compiles documented chunks and synthesizes a response to an inputted prompt. It can evaluate what content is most relevant to the prompt in question, and return a well-developed answer that’s advisory in nature. That is, it helps the user who asked the question to take the next step.
Agentic systems take that next step for the user. Once it’s set up to complete a specific task, it can autonomously do so. This means they observe and analyze their environment to make informed decisions. Autonomous AI agents can interact with other systems or devices to complete the task or tasks it’s been given. Multiple agents can also be set up in a multi-agent system to accomplish complex tasks, with individual actions separated and given to the agent best suited for the task.
Both generative AI and agentic AI have immense potential to uplevel and enhance business processes for operational efficiency, but these technologies should be implemented with care — we’ll detail why a little further below.
Why Does An Agentic AI System Matter for the Enterprise?
Agentic AI workflows aren’t just another automation tool — they represent a major shift toward intelligent, goal-driven systems that optimize workflows, enhance decision-making, and personalize digital experiences. For enterprises, this translates into tangible business benefits:
Increased efficiency and productivity
An autonomous agent handles routine work, reducing manual effort and allowing employees to focus on high-value tasks.
Smarter decision-making
AI continuously analyzes real-time data, identifies patterns, and provides actionable insights, helping businesses make faster, more informed decisions.
Advanced automation for greater innovation
Unlike traditional automation, agentic AI dynamically adapts to new data and exceptions, improving accuracy and reducing the need for human intervention — reducing time and effort to create new products or applications.
Cost reduction and revenue growth
By automating processes, optimizing workflows, and improving customer experiences, agentic AI helps lower operational costs while increasing revenue opportunities.
These advantages make agentic AI a game-changer across industries, from finance and healthcare to ecommerce and customer service. It’s particularly transformative in areas like customer experience (CX) and digital workplaces, where AI-powered interactions can drive higher engagement, faster resolutions, and more seamless self-service.
What Agentic AI Challenges Do Enterprises Need to Overcome?
While agentic AI offers significant benefits, enterprises must address key challenges before deploying it effectively:
Data security and compliance
AI agents must operate within strict governance frameworks to prevent unauthorized access to sensitive or inaccurate data.
Transparency and explainability
AI decisions should not be a “black box.” Enterprises need clear mechanisms to track reasoning, ensure alignment with business objectives, and build trust.
Human oversight
While agentic AI can operate autonomously, there must be clear escalation paths for complex decisions that require human judgment.
Bias and fairness
AI models inherit biases from training data. Continuous audits and fairness-aware techniques are critical to preventing unintended discrimination.
Business alignment
AI agents should be designed to work toward enterprise goals, ensuring that actions taken by AI are relevant, accurate, and aligned with company priorities.
How Coveo Helps Enterprises Overcome These Challenges
Successfully adopting agentic AI requires a strong knowledge and retrieval foundation, robust governance, and enterprise-grade security. Coveo’s AI-Relevance Platform ensures:
- With Coveo’s Passage Retrieval API, AI is grounded in trusted, proprietary knowledge rather than relying on open, unverified data.
- Retrieval-augmented generation (RAG) techniques are boosted with relevancy to enhance accuracy and prevent hallucinations.
- AI-driven experiences are designed with built-in compliance, transparency, and oversight.
By addressing these challenges proactively, enterprises can harness agentic AI with confidence, ensuring security, accuracy, and long-term business impact.
The Impact of Agentic AI on Customer Service and Support
Proactive Self-Service for Customers
Prospects and customers who interact with an enterprise’s agentic AI aren’t simply given a list of links when they ask a question — an AI agent helps retrieve the most relevant information (based on content that was deemed successful for similar users) and package it in a way that answers the specific question asked.
Users can ask clarifying or follow up questions so they get the full picture — and possibly even more, when the agent can offer best next-step recommendations that keep that prospect or customer engaged.
Relevant reading: Salesforce Agentforce is only as effective as the knowledge it can access
Better Case Resolution
Agentic AI can step in before a human agent is needed to help customers resolve issues in real time. With seamless access to unified content, an agent can do much more than just provide an answer — it can help walk a customer through instructions toward resolution. A customer can ask additional clarifying questions that the AI agent can respond to.
These responses can be hyperpersonalized to the person asking the question, based not just on keywords but also customer preferences and real-time behavior. This offers a much more interactive, seamless 24/7 customer service experience that’s impossible to achieve with human staff alone.
Smarter Case Classification for Agents
AI agents won’t just be another channel for customers to interact with businesses through — with their autonomous capabilities, they can become connective tissue between those who need help and the human agents who can assist them. An AI agent can triage requests to ensure that all pertinent details needed to resolve the case are packaged up for a human agent, and then also use those details to ensure that the case is routed to the best person suited for the job.
Agentic AI can also assist on the agent side, digging up previously closed cases that are similar to the new case at hand, as well as other knowledge articles and sources that can help resolve the issue quickly.
The Impact of Agentic AI on Ecommerce
AI has already reshaped ecommerce with personalized recommendations, intelligent search, and automation. Agentic AI takes it to the next level, proactively optimizing the customer journey and making goal-driven decisions in real time to maximize engagement and revenue.
For ecommerce businesses, this means higher conversions, bigger orders, and happier customers — all while creating new opportunities for growth in an AI-driven world.
Search and Product Discovery
Imagine an online store where search isn’t simply a list of results, but an interactive experience where hyper-relevant products are surfaced instantly. Agentic AI understands not just what users type, but what they actually mean. It understands intent, context and behavioral signals.
A shopper searching for “running shoes for winter” wouldn’t just see generic sneakers but curated options with waterproofing, thermal protection, and slip-resistant soles.
Dynamic Merchandising and Promotions
Retailers spend hours manually adjusting product rankings and promotions. Agentic AI could automate this process, ensuring the most relevant products get visibility based on inventory, customer demand, and seasonal trends in real time.
This means more personalized upsells and cross-sells, higher AOV and maximized revenue per visit.
AI-Powered Shopping Assistants
Unlike traditional rule-based assistants, AI-driven shopping assistants actively guide shoppers, answering complex questions, making recommendations, and addressing hesitations or concerns – reducing cart abandonment.
AI-Driven SEO
Agentic AI can automate the creation of high-converting, dynamically generated category pages based on real-time trends. Retailers can capture more search traffic, leading to higher-quality inbound traffic and better organic conversion rates.
A human SEO specialist can review and optimize limited pages a day; an agentic AI model can do 20,000.
Proactive Customer Retention
By analyzing behavior, agentic AI could predict at-risk customers and proactively send personalized offers, restock reminders, or loyalty rewards before they even realize they need them. Agentic AI anticipates needs before they arise, keeping customers engaged and boosts lifetime value.
By making AI autonomous, proactive, and adaptive, agentic AI has the potential to push ecommerce into a new era — one where businesses don’t just respond to customer behavior, but stay ahead of it.
The Impact of Agentic AI on Digital Workplaces
Enhance Human Resources Support
With AI agents embedded in human resource workflows, enterprise HR teams can streamline core processes, speed up routine tasks, and create a more responsive employee experience.
Some examples include responding to benefits questions or assisting with onboarding paperwork — freeing up HR professionals to focus on strategic priorities like organizational planning and talent development. In the long-term, this helps growing businesses create efficient, scalable, and cost-effective HR operations.
Relieve Common IT Issues
Standard AI follows troubleshooting scripts; agentic AI can adapt dynamically to new situations. Because it can integrate data from various IT management systems, learn from past incidents, and modify its problem-solving approach autonomously, AI agents can resolve IT tickets faster and more accurately.
It can also shoulder the tedious burden of resolving repetitive tasks like password resets and software access, delivering a higher degree of personalization based on individual user behavior and patterns.
Personal Digital Assistant for Employees
AI agents can function as smart assistants for staff, handling routine internal tasks and queries so that employees can concentrate on more meaningful work. For example, an employee can outsource building a pitch deck or collecting research on a prospective customer so they can focus on meaningful competitive intelligence that will help their company stand out.
In essence, agentic AI can serve as a digital coworker that improves productivity and morale – doing the boring stuff in the background, ensuring employees have the information and resources they need, and thereby elevating overall job satisfaction
Agentic AI: The Next Evolution in Enterprise AI Strategy
Agentic AI represents the next evolution in enterprise AI strategy, offering the potential to transform digital experiences, automate complex workflows, and drive smarter business decisions. But AI is only as effective as the knowledge it’s built on.
To fully harness agentic AI, enterprises need a trusted foundation of secure, relevant, and enterprise-approved information — ensuring AI-driven decisions are accurate, compliant, and aligned with business goals.
That’s where Coveo comes in. The Coveo AI-Relevance Platform provides the intelligence and infrastructure needed to turn AI ambition into real business impact. By grounding AI in trusted knowledge and optimizing every digital interaction, Coveo ensures enterprises can deploy AI with confidence and at scale.
Ready to see how agentic AI can transform your business? Explore the Coveo AI-Relevance Platform.
Dig Deeper
AI is evolving fast; be part of the journey. Coveo is partnering with customers looking to advance and refine agentic AI, joining forces to deliver real business impact together. The Agentic AI Design Partner Program gives participants access to Coveo AI expertise, shared design sessions, peer learning, and insight into future capabilities.